Abstract:
Recent advancements in natural language processing have highlighted the need for systems that can effectively retrieve and generate information to handle increasingly complex queries. Combining retrieval and generation processes addresses the limitations of each approach individually, leading to more comprehensive and accurate responses. This thesis presents the implementation of a Retrieval-Augmented Generation (RAG) agent utilizing Llama3 to enhance the accuracy and relevance of responses in complex query environments. The primary challenge is integrating effective information retrieval with advanced generative capabilities to provide precise and reliable answers. Our approach combines document retrieval, grading, and generation within a cohesive system. Queries are assessed for relevance, retrieving pertinent documents or conducting web searches as needed. The generated answers are rigorously evaluated to ensure they meet high standards of accuracy. This implementation demonstrates the potential of merging sophisticated retrieval mechanisms with powerful generative models, resulting in significant improvements in response quality and reliability.
